scholarly journals Local thermal gradient and large-scale circulation impacts on turbine-height wind speed forecasting over the Columbia Basin

2021 ◽  
Author(s):  
Ye Liu ◽  
Yun Qian ◽  
Larry K. Berg
Keyword(s):  
Wind Speed ◽  
Thermal Gradient ◽  
Large Scale ◽  
Forecast Accuracy ◽  
Columbia River ◽  
Self Organizing Map ◽  
Weather System ◽  
Large Scale Circulation ◽  
Thermal Anomalies ◽  
Marine Air

Abstract. We investigate the sensitivity of turbine-height wind speed forecast to initial condition (IC) uncertainties over the Columbia River Gorge (CRG) and Columbia River Basin (CRB) for two typical weather phenomena, i.e., local thermal gradient induced marine air intrusion and a cold frontal passage. Four types of turbine-height wind forecast anomalies and their associated IC uncertainties related to local thermal gradients and large-scale circulations are identified using the self-organizing map (SOM) technique. The four SOM types are categorized into two patterns, each accounting for half of the ensemble members. The first pattern corresponds to IC uncertainties that alter the wind forecast through modulating weather system, which produces the strongest wind anomalies in the CRG and CRB. In the second pattern, the moderate local thermal gradient and large-scale circulation uncertainties jointly contribute to wind forecast anomaly. We analyze the cross-section of wind and temperature anomalies through the gorge to explore the evolution of vertical features of each SOM type. The turbine-height wind anomalies induced by large-scale IC uncertainties are more concentrated near the front. In contrast, turbine-height wind anomalies induced by the local IC thermal uncertainties are found above the surface thermal anomalies. Moreover, the wind forecast accuracy in the CRG and CRB are limited by IC uncertainties in a few specific regions, e.g., the 2-m temperature within the basin and large-scale circulation over the northeast Pacific around 140° W.

Impact of weather regimes on wind power variability in western Europe

2020 ◽  
Author(s):  
Ricardo García-Herrera ◽  
Jose M. Garrido-Perez ◽  
Carlos Ordóñez ◽  
David Barriopedro ◽  
Daniel Paredes
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Large Scale ◽  
Western Europe ◽  
Power Production ◽  
Large Scale Circulation ◽  
Weather Regimes ◽  
The Uk

<p><span><span>We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula. </span></span></p><p><span><span>The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.</span></span></p>

scholarly journals Striving for Improvement: The Perceived Value of Improving Hurricane Forecast Accuracy

2021 ◽  
pp. 1-41
Author(s):  
Renato Molina ◽  
David Letson ◽  
Brian McNoldy ◽  
Pallab Mozumder ◽  
Matthew Varkony
Keyword(s):  
United States ◽  
Wind Speed ◽  
Willingness To Pay ◽  
Large Scale ◽  
Storm Track ◽  
Forecast Accuracy ◽  
Physical Capital ◽  
The United States ◽  
Atmospheric Modeling ◽  
Private And Public

AbstractHurricanes are the costliest type of natural disaster in the United States. Every year, these natural phenomena destroy billions of dollars in physical capital, displace thousands, and greatly disrupt local economies. While this damage will never be eliminated, the number of fatalities and the cost of preparing and evacuating can be reduced through improved forecasts. This paper seeks to establish the public’s willingness to pay for further improvement of hurricane forecasts by integrating atmospheric modeling and a double-bounded dichotomous choice method in a large-scale contingent valuation experiment. Using an interactive survey, we focus on areas affected by hurricanes in 2018 to elicit residents’ willingness to pay for improvements along storm track, wind speed and precipitation forecasts. Our results indicate improvements in wind speed forecast are valued the most, followed by storm track and precipitation, and that maintaining a rate of improvement of 5% error reduction for another decade is worth between US$90.25 to US$121.86 per person in vulnerable areas. Our study focuses on areas recently hit by hurricanes in the United States, but the implications of our results can be extended to areas vulnerable to tropical cyclones globally. In a world where the intensity of hurricanes is expected to increase and research funds are limited, these results can inform relevant agencies regarding the effectiveness of different private and public adaptive actions, as well as the value of publicly funded hurricane research programs.

Doppler-Lidar Evaluation of HRRR-Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2

2021 ◽  
Author(s):  
Robert M. Banta ◽  
Yelena L. Pichugina ◽  
Lisa S. Darby ◽  
W. Alan Brewer ◽  
Joseph B. Olson ◽  
...  
Keyword(s):  
Wind Speed ◽  
Pressure Gradient ◽  
Large Scale ◽  
Surface Wind ◽  
Columbia River ◽  
Surface Heating ◽  
Model Errors ◽  
Improvement Project ◽  
Near Surface ◽  
Wind Regimes

AbstractComplex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

Thermal anomaly detection in datacenters

2011 ◽  
Vol 226 (8) ◽  
pp. 2104-2117 ◽  
Author(s):  
Yang Yuan ◽  
Eun Kyung Lee ◽  
Dario Pompili ◽  
Junbi Liao
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Large Scale ◽  
Thermal Anomaly ◽  
Detection Methods ◽  
Support Vector ◽  
Self Organizing Map ◽  
Air Conditioner ◽  
Thermal Anomalies ◽  
The Relationship

The high density of servers in datacenters generates a large amount of heat, resulting in the high possibility of thermally anomalous events, i.e. computer room air conditioner fan failure, server fan failure, and workload misconfiguration. As such anomalous events increase the cost of maintaining computing and cooling components, they need to be detected, localized, and classified for taking appropriate remedial actions. In this article, a hierarchical neural network framework is proposed to detect small- (server level) and large-scale (datacenter level) thermal anomalies. This novel framework, which is organized into two tiers, analyzes the data sensed by heterogeneous sensors such as sensors built in the servers and external sensors (Telosb). The proposed solution employs a neural network to learn about (a) the relationship among sensing values (i.e. internal, external, and fan speed) and (b) the relationship between the sensing values and workload information. Then, the bottom tier of our framework detects thermal anomalies, whereas the top tier localizes and classifies them. Our solution outperforms other anomaly-detection methods based on regression model, support vector machine, and self-organizing map, as shown by the experimental results.

Objective Identification of Nonlinear Convectively Coupled Phases of Monsoon Intraseasonal Oscillation: Implications for Prediction

2008 ◽  
Vol 65 (5) ◽  
pp. 1549-1569 ◽  
Author(s):  
R. Chattopadhyay ◽  
A. K. Sahai ◽  
B. N. Goswami
Keyword(s):  
Summer Monsoon ◽  
Large Scale ◽  
Intraseasonal Oscillation ◽  
Central India ◽  
Self Organizing Map ◽  
Large Scale Circulation ◽  
Nonlinear Phase ◽  
Extended Range ◽  
History Of ◽  
Circulation Indices

Abstract The nonlinear convectively coupled character of the summer monsoon intraseasonal oscillation (ISO) that manifests in its event-to-event variations is a major hurdle for skillful extended-range prediction of the active/break episodes. The convectively coupled character of the monsoon ISO implies that a particular nonlinear phase of the precipitation ISO is linked to a unique pattern of the large-scale variables. A methodology has been presented to capture different nonlinear phases of the precipitation ISO using a combination of a sufficiently large number of dynamical variables. This is achieved through a nonlinear pattern recognition technique known as self-organizing map (SOM) involving six daily large-scale circulation indices. It is demonstrated that the nonlinearly classified states of the large-scale circulation isolated at the SOM nodes without involving any information on rainfall are strongly linked to different phases of evolution of the rainfall ISO, including the active and break phases. While a lower SOM classification involving 9 different states identify the composite phases of the rainfall ISO, a higher SOM classification involving 81 states can identify different shades of composite phase of the rainfall ISO. The concept of isolating the nonlinear states, as well as the technique of doing so, is robust as almost identical phases of precipitation ISO are identified by the large-scale circulation indices derived from two different reanalysis datasets, namely, the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis. The ability of the SOM technique to isolate spatial structure and evolutionary history of nonlinear convectively coupled states of the summer monsoon ISO opens up a new possibility of extended-range prediction of summer monsoon ISO. This knowledge is used to develop an analog technique for predicting different phases of monsoon ISO. Skillful four-pentad lead prediction of rainfall over central India is demonstrated with the model using only large-scale circulation fields. A major strength of the model is that it can easily be used for real-time extended-range prediction of monsoons.

scholarly journals Characteristics of Large-Scale Circulation Affecting the Inter-Annual Precipitation Variability in Northern Sumatra Island during Boreal Summer

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Yahya Darmawan ◽  
Huang-Hsiung Hsu ◽  
Jia-Yuh Yu
Keyword(s):  
Large Scale ◽  
Precipitation Variability ◽  
Specific Humidity ◽  
Boreal Summer ◽  
Moisture Budget ◽  
Large Scale Circulation ◽  
Budget Analysis ◽  
Sumatra Island ◽  
Precipitation Anomalies ◽  
The Impact

This study aims to explore the contrasting characteristics of large-scale circulation that led to the precipitation anomalies over the northern parts of Sumatra Island. Further, the impact of varying the Asian–Australian Monsoon (AAM) was investigated for triggering the precipitation variability over the study area. The moisture budget analysis was applied to quantify the most dominant component that induces precipitation variability during the JJA (June, July, and August) period. Then, the composite analysis and statistical approach were applied to confirm the result of the moisture budget. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Anaysis Interim (ERA-Interim) from 1981 to 2016, we identified 9 (nine) dry and 6 (six) wet years based on precipitation anomalies, respectively. The dry years (wet years) anomalies over the study area were mostly supported by downward (upward) vertical velocity anomaly instead of other variables such as specific humidity, horizontal velocity, and evaporation. In the dry years (wet years), there is a strengthening (weakening) of the descent motion, which triggers a reduction (increase) of convection over the study area. The overall downward (upward) motion of westerly (easterly) winds appears to suppress (support) the convection and lead to negative (positive) precipitation anomaly in the whole region but with the largest anomaly over northern parts of Sumatra. The AAM variability proven has a significant role in the precipitation variability over the study area. A teleconnection between the AAM and other global circulations implies the precipitation variability over the northern part of Sumatra Island as a regional phenomenon. The large-scale tropical circulation is possibly related to the PWC modulation (Pacific Walker Circulation).

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

scholarly journals Dynamic Loads and Response of a Spar Buoy Wind Turbine with Pitch-Controlled Rotating Blades: An Experimental Study

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3598
Author(s):  
Sara Russo ◽  
Pasquale Contestabile ◽  
Andrea Bardazzi ◽  
Elisa Leone ◽  
Gregorio Iglesias ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Laboratory Data ◽  
Nonlinear Behavior ◽  
Offshore Wind ◽  
Small Scale ◽  
Wave Steepness ◽  
Design Factor

New large-scale laboratory data are presented on a physical model of a spar buoy wind turbine with angular motion of control surfaces implemented (pitch control). The peculiarity of this type of rotating blade represents an essential aspect when studying floating offshore wind structures. Experiments were designed specifically to compare different operational environmental conditions in terms of wave steepness and wind speed. Results discussed here were derived from an analysis of only a part of the whole dataset. Consistent with recent small-scale experiments, data clearly show that the waves contributed to most of the model motions and mooring loads. A significant nonlinear behavior for sway, roll and yaw has been detected, whereas an increase in the wave period makes the wind speed less influential for surge, heave and pitch. In general, as the steepness increases, the oscillations decrease. However, higher wind speed does not mean greater platform motions. Data also indicate a significant role of the blade rotation in the turbine thrust, nacelle dynamic forces and power in six degrees of freedom. Certain pairs of wind speed-wave steepness are particularly unfavorable, since the first harmonic of the rotor (coupled to the first wave harmonic) causes the thrust force to be larger than that in more energetic sea states. The experiments suggest that the inclusion of pitch-controlled, variable-speed blades in physical (and numerical) tests on such types of structures is crucial, highlighting the importance of pitch motion as an important design factor.

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